Distribution optimally balanced stratified cross-validation (DOB-SCV) partitions a data set into n folds in such a way that a balanced distribution in feature space is maintained for each class, in addition to stratification based on the label.
The real-world effect of using DOB-SCV, instead of stratified cross-validation, is slightly higher testing accuracy. The biggest improvements can be expected on small, class imbalanced data sets.
The implementation can be used as a drop-in replacement for CVPARTITION.
Reference: Study on the Impact of Partition-Induced Dataset Shift on k-Fold Cross-Validation available from https://ieeexplore.ieee.org/document/6226477
Jan Motl (2020). Distribution-balanced stratified cross-validation (https://www.mathworks.com/matlabcentral/fileexchange/72963-distribution-balanced-stratified-cross-validation), MATLAB Central File Exchange. Retrieved .